Original Research
The Law Firm AI Visibility Index
We scored 106 personal injury law firm websites across 12 major US metros on 12 technical signals we score for AI search readiness: crawl access, page metadata and structured data. The verdict: most firms get the basics right and miss the structured data that names who is behind the site. Actual AI citations were not measured.
Last updated August 2, 2026. Methodology below.
Key findings, August 2, 2026: across 106 personal injury law firm websites in 12 US metros, the median technical AI search readiness score was 80 out of 100. 45.3% named an attorney or author in structured data, and 20.8% published FAQ schema. Source: VerdictIQ Law Firm AI Visibility Index.
The basics are done; structured data is not
Nearly every firm we scored nails the fundamentals. 100% serve HTTPS, 99.1% have a title tag, and 96.2% ship at least some structured data. Traditional SEO has done its job on the technical basics.
The gap opens on structured data that names the firm and the people behind it, and it is narrower than the first run suggested. 45.3% now expose attorney or author schema, markup that names who is behind the advice, so roughly half the market has that covered. Only 20.8% publish FAQ schema, which marks up question and answer content for machine readers (Google retired FAQ rich results in May 2026, so its value is readability, not a search feature). 64.2% have a social preview image.
Interestingly, 44.3% now serve an llms.txt file, mostly auto-generated by SEO plugins. That is the tell: firms are bolting on superficial AI signals without the underlying structure (answerable content, named experts, structured attribution). A plugin file is not a strategy.
How 106 firms scored, signal by signal
Amber bars mark signals present on fewer than 60% of firms. These are the AI-visibility gaps.
Score distribution
These scores measure technical readiness, not whether an AI tool will recommend a firm. To check one site against the same signals, run the free AI visibility checker. To have the gaps fixed on your own site, see our AI visibility services for law firms.
Results by metro
Each metro holds 7 to 10 firms, so treat these as directional: one firm moves a metro average by several points.
| Metro | Firms | Average | Median | Author schema | FAQ schema |
|---|---|---|---|---|---|
| Atlanta | 10 | 75 | 80 | 40% | 10% |
| Dallas | 8 | 74.4 | 75 | 25% | 12.5% |
| Chicago | 10 | 80 | 80 | 40% | 30% |
| Phoenix | 8 | 83.8 | 82.5 | 50% | 37.5% |
| New York City | 8 | 78.1 | 80 | 37.5% | 0% |
| Los Angeles | 7 | 79.3 | 80 | 28.6% | 14.3% |
| Houston | 9 | 81.7 | 90 | 66.7% | 44.4% |
| Miami | 9 | 71.1 | 80 | 33.3% | 0% |
| Philadelphia | 10 | 79 | 80 | 10% | 20% |
| Denver | 9 | 79.4 | 80 | 33.3% | 22.2% |
| Seattle | 9 | 89.4 | 90 | 100% | 44.4% |
| Boston | 9 | 82.8 | 80 | 77.8% | 11.1% |
Download the aggregate results (CSV): signal pass rates and per-metro scores. Individual firm scores are not published.
How to cite this study
VerdictIQ. “Law Firm AI Visibility Index.” Technical AI search readiness scores for 106 US personal injury law firm websites across 12 metros, August 2, 2026. https://verdictiq.org/law-firm-ai-visibility-index
Methodology
We sampled 106 personal injury law firm websites across 12 major US metros (Atlanta, Dallas, Chicago, Phoenix, New York City, Los Angeles, Houston, Miami, Philadelphia, Denver, Seattle, Boston), sourced from public search results. Each homepage was fetched and scored out of 100 across 12 signals grouped into Technical Foundation, Content Signals, Structured Data, and AI Crawlability, weighted toward the structured-data and crawlability signals. Detection is purely technical (no AI-generated estimates): we parse the page HTML, JSON-LD, llms.txt, and robots.txt directly. llms.txt and robots.txt are only counted when served as plain text, to avoid false positives from sites that return their homepage for unknown paths. The scan runs on the same engine as our free checker, and the figures below are from the August 2, 2026 run under the current scoring. That scoring was tightened after the first published run: an unreadable robots.txt no longer counts as crawler access, llms.txt carries less weight, Organization and author credit require a named entity rather than a matching type string, and FAQ credit requires at least one question to be visible on the page. The parser was also rewritten to walk every node in a JSON-LD block instead of only the root and its @graph array. That last change matters for comparison: the first run reported 17.9% author schema and a median of 70, but it was missing entities nested inside other nodes. The difference between the two runs is mostly a corrected instrument rather than eight weeks of market movement.
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